def test_zipped():
pmf = [0.125, 0.125, 0.125, 0.125, 0.25, 0, 0.25]
outcomes = ['000', '011', '101', '110', '222', '321', '333']
d = Distribution(outcomes, pmf)
outcomes_, pmf_ = list(zip(*d.zipped()))
d2 = Distribution(outcomes_, pmf_)
assert d.is_approx_equal(d2)
outcomes_, pmf_ = list(zip(*d.zipped(mode='atoms')))
d3 = Distribution(outcomes_, pmf_)
assert d.is_approx_equal(d3)
outcomes_, pmf_ = list(zip(*d.zipped(mode='patoms')))
d4 = Distribution(outcomes_, pmf_)
d.make_sparse()
assert np.allclose(d.pmf, d4.pmf)
def test_zipped():
pmf = [.125, .125, .125, .125, .25, 0, .25]
outcomes = ['000', '011', '101', '110', '222', '321', '333']
d = Distribution(outcomes, pmf)
outcomes_, pmf_ = list(zip(*d.zipped()))
d2 = Distribution(outcomes_, pmf_)
assert_true(d.is_approx_equal(d2))
outcomes_, pmf_ = list(zip(*d.zipped(mode='atoms')))
d3 = Distribution(outcomes_, pmf_)
assert_true(d.is_approx_equal(d3))
outcomes_, pmf_ = list(zip(*d.zipped(mode='patoms')))
d4 = Distribution(outcomes_, pmf_)
d.make_sparse()
np.testing.assert_allclose(d.pmf, d4.pmf)
def test_init10():
d1 = Distribution([(0, 0), (0, 1), (1, 0)], [0.5, 0.25, 0.25])
pmf = [[0.5, 0.25], [0.25, 0]]
d2 = Distribution.from_ndarray(pmf)
assert d1.is_approx_equal(d2)
def test_init7():
outcomes = ['0', '1']
pmf = [1 / 2] * 2
d1 = Distribution(outcomes, pmf)
d2 = Distribution.from_distribution(d1)
assert d1.is_approx_equal(d2)
def test_init7():
outcomes = ['0', '1']
pmf = [1/2, 1/2]
d1 = Distribution(outcomes, pmf)
d2 = Distribution.from_distribution(d1)
assert_true(d1.is_approx_equal(d2))
def test_init10():
d1 = Distribution([(0, 0), (0, 1), (1, 0)], [0.5, 0.25, 0.25])
pmf = [[0.5, 0.25], [0.25, 0]]
d2 = Distribution.from_ndarray(pmf)
assert d1.is_approx_equal(d2)
